There are many multiple-model (MM) target-tracking algorithms that are
available but there has yet to be a comparison that includes all of them. This work
compares seven of the currently most popular MM algorithms in terms of
performance, credibility, and computational complexity. The algorithms to be
considered are the autonomous multiple-model algorithm, generalized pseudo-
Bayesian of first order, generalized pseudo-Bayesian of second order, interacting
multiple-model algorithm, B-Best algorithm, Viterbi algorithm, and reweighted
interacting multiple-model algorithm. The algorithms were compared using three
scenarios consisting of maneuvers that were both in and out of the model set. Based
on this comparison, there is no clear-cut best algorithm but the B-best algorithm
performs best in terms of tracking errors and the IMM algorithm has the best
computational complexity among the algorithms that have acceptable tracking errors.

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